rag-code-mcp

Architecture

This document describes the internal architecture of RagCode MCP Server after the multi-language restructuring.

Overview

RagCode MCP is structured to support multiple programming languages through a pluggable analyzer architecture. The codebase is organized to separate language-agnostic components from language-specific analyzers.

Directory Structure

internal/
├── codetypes/              # Universal types and interfaces (language-agnostic)
│   ├── types.go           # CodeChunk (canonical), PathAnalyzer (legacy APIChunk/APIAnalyzer kept only for compatibility)
│   └── symbol_schema.go   # Symbol schema definitions
│
├── ragcode/               # Core indexing and language management
│   ├── indexer.go         # Indexing logic using PathAnalyzer (CodeChunk-only)
│   ├── language_manager.go # Factory for selecting language analyzers (by project type)
│   ├── ragcode_test.go    # Integration tests
│   ├── laravel_integration_test.go # Laravel integration tests
│   └── analyzers/         # Language-specific analyzers
│       ├── golang/        # Go language analyzer (fully implemented)
│       │   ├── analyzer.go           # PathAnalyzer implementation → CodeChunk
│       │   ├── api_analyzer.go       # API documentation analyzer
│       │   ├── types.go              # Go-specific types (FunctionInfo, TypeInfo, etc.)
│       │   └── analyzer_test.go      # Unit tests
│       ├── php/           # PHP analyzer (including Laravel support)
│       │   ├── analyzer.go           # Main PHP analyzer
│       │   ├── api_analyzer.go       # PHP API analyzer
│       │   ├── phpdoc.go             # PHPDoc parsing
│       │   ├── types.go              # PHP-specific types
│       │   └── laravel/   # Laravel-specific analyzers
│       │       ├── analyzer.go       # Laravel analyzer coordinator
│       │       ├── eloquent.go       # Eloquent model analyzer
│       │       ├── controller.go     # Controller analyzer
│       │       ├── routes.go         # Route analyzer
│       │       ├── adapter.go        # Adapter for integration
│       │       └── ast_helper.go     # AST utilities
│       ├── html/          # HTML analyzer
│       │   └── analyzer.go
│       └── python/        # Python analyzer (full implementation)
│           ├── analyzer.go
│           ├── analyzer_test.go
│           ├── api_analyzer.go
│           ├── types.go
│           └── README.md
│
├── workspace/             # Multi-workspace detection and management
│   ├── manager.go         # Workspace manager (per-language collections)
│   ├── detector.go        # Workspace root detection
│   ├── language_detection.go # Language detection from markers
│   ├── multi_search.go    # Cross-workspace search logic
│   ├── cache.go           # Workspace cache
│   ├── types.go           # Workspace types and structs
│   ├── README.md          # Workspace documentation
│   └── *_test.go          # Comprehensive test suite (manager_multilang_test.go, etc.)
│
├── tools/                 # MCP tool implementations (9 tools)
│   ├── search_local_index.go
│   ├── hybrid_search.go
│   ├── get_function_details.go
│   ├── find_type_definition.go
│   ├── get_code_context.go
│   ├── list_package_exports.go
│   ├── find_implementations.go
│   ├── search_docs.go
│   ├── index_workspace.go    # Manual indexing tool
│   ├── workspace_helpers.go  # Helper functions for tools
│   ├── utils.go
│   └── *_test.go             # Tool tests
│
├── storage/               # Vector database (Qdrant) integration
│   ├── qdrant.go          # Qdrant client wrapper
│   ├── qdrant_memory.go   # LongTermMemory implementation
│   ├── qdrant_memory_test.go
│   └── (Redis, SQLite configs - optional backends)
│
├── memory/                # Memory management (short-term, long-term)
│   ├── state.go           # Memory state interface
│   ├── shortterm.go       # Short-term memory implementation
│   ├── longterm.go        # Long-term memory interface
│   └── (Storage implementations)
│
├── llm/                   # LLM provider (Ollama, HuggingFace, etc.)
│   ├── provider.go        # LLM provider interface
│   ├── ollama.go          # Ollama implementation
│   └── provider_test.go   # Tests
│
├── config/                # Configuration management
│   ├── config.go          # Config structs (8 sections: LLM, Storage, etc.)
│   ├── loader.go          # YAML + ENV parsing
│   └── config_test.go     # Tests
│
├── healthcheck/           # Health check utilities
│   └── healthcheck.go     # Dependency checks (Ollama, Qdrant, etc.)
│
├── utils/                 # Utility functions
│   └── retry.go           # Retry logic
│
└── codetypes/             # (See above)

Multi-Language & Multi-Workspace Architecture

Overview

RagCode MCP supports polyglot workspaces (containing multiple programming languages) by creating separate Qdrant collections per language per workspace. This ensures clean separation of code by language, better search quality, and improved scalability.

Collection Naming Strategy

Format:

{prefix}-{workspaceID}-{language}

Examples:

ragcode-a1b2c3d4e5f6-go
ragcode-a1b2c3d4e5f6-python
ragcode-a1b2c3d4e5f6-javascript
ragcode-a1b2c3d4e5f6-php

Default Prefix: ragcode (configurable via workspace.collection_prefix in config.yaml)

Language Detection Strategy

Language detection uses file markers to identify programming languages present in a workspace:

Marker File	Detected Language
go.mod	go
package.json	javascript
pyproject.toml	python
setup.py	python
requirements.txt	python
composer.json	php
Cargo.toml	rust
pom.xml	java
build.gradle	java
Gemfile	ruby
Package.swift	swift
.git	workspace root

Multi-Language Workspace Example

Consider a monorepo with multiple languages:

myproject/
├── .git
├── go.mod                  # Triggers Go detection
├── main.go                 # → Indexed into ragcode-xxx-go
├── api_server.go
├── scripts/
│   ├── pyproject.toml      # Triggers Python detection
│   ├── train.py            # → Indexed into ragcode-xxx-python
│   └── ml_utils.py
└── web/
    ├── package.json        # Triggers JavaScript detection
    ├── app.js              # → Indexed into ragcode-xxx-javascript
    └── utils.ts

Results in 3 collections:

• ragcode-{workspaceID}-go - Contains all Go code
• ragcode-{workspaceID}-python - Contains all Python code
• ragcode-{workspaceID}-javascript - Contains all JavaScript/TypeScript code

Indexing Strategy

When indexing a workspace:

1. Detect all languages present in the workspace from markers
2. For each detected language:
   • Create collection if it doesn’t exist: {prefix}-{workspaceID}-{language}
   • Select appropriate analyzer (Go, PHP, Python, etc.)
   • Filter files by language extension (**/*.go, **/*.py, etc.)
   • Index using language-specific analyzer
   • Store all chunks with Language field set to the language identifier

File Filtering Examples:

Language	Include Patterns	Exclude Patterns
Go	**/*.go	**/*_test.go, vendor/
Python	**/*.py	**/__pycache__/, **/.venv/
JavaScript	**/*.js, **/*.ts	**/node_modules/, **/dist/
PHP	**/*.php	**/vendor/, **/cache/

Query Strategy

Language-Specific Search

When a query is received via MCP tools with file context:

1. Detect file context from query parameters (e.g., file_path)
2. Infer language from file extension or workspace markers
3. Search in language-specific collection: {prefix}-{workspaceID}-{language}

Example: Query with Go file context

{
  "file_path": "/workspace/main.go",
  "query": "handler function"
}

→ Automatically searches in ragcode-{workspaceID}-go

Cross-Language Search

For semantic searches across all code:

1. Query all language collections in the workspace
2. Merge and rank results by relevance score
3. Return unified results with language metadata for context

Example: Semantic search without file context

{
  "query": "authentication middleware",
  "workspace_id": "backend"
}

→ Searches in:

• ragcode-backend-go
• ragcode-backend-python
• ragcode-backend-javascript → Returns combined results with language labels

Workspace Info API

The Workspace.Info struct tracks detected languages:

type Info struct {
    Root             string    `json:"root"`
    ID               string    `json:"id"`
    ProjectType      string    `json:"project_type,omitempty"`
    Languages        []string  `json:"languages,omitempty"` // Detected languages
    Markers          []string  `json:"markers,omitempty"`   // Detection markers found
    DetectedAt       time.Time `json:"detected_at,omitempty"`
    CollectionPrefix string    `json:"collection_prefix,omitempty"`
}

// CollectionNameForLanguage returns the collection name for a specific language
func (w *Info) CollectionNameForLanguage(language string) string {
    return w.CollectionPrefix + "-" + w.ID + "-" + language
}

Migration from Single-Collection Mode

Legacy Format (Deprecated):

ragcode-{workspaceID}  →  [Mixed Go + Python + JavaScript code]

New Format:

ragcode-{workspaceID}-go          →  [Go code only]
ragcode-{workspaceID}-python      →  [Python code only]
ragcode-{workspaceID}-javascript  →  [JavaScript code only]

To migrate:

1. Delete old collection (optional): ragcode-{workspaceID}
2. Re-run indexing: Automatically creates language-specific collections
3. Update queries: Use CollectionNameForLanguage(language) instead of single collection

Benefits of Multi-Language Architecture

1. Better Organization - Clear separation of code by language
2. Improved Search Quality - Language-specific chunking and embeddings
3. Scalability - Independent indexing per language, supports parallel processing
4. Debugging - Easy to identify and fix language-specific indexing issues
5. Extensibility - Add new languages without affecting existing ones

---

Core Components

1. Universal Types (internal/codetypes)

Purpose: Define language-agnostic types and interfaces used across all analyzers.

Key Types:

• CodeChunk - Represents a code symbol (function, method, type, etc.)
• APIChunk - Represents API documentation for a symbol
• PathAnalyzer - Interface for code analysis
• APIAnalyzer - Interface for API documentation extraction

Design Principle: These types are enhanced with LSP-inspired fields (Language, URI, SelectionRange, Detail, AccessModifier, Tags, Children) to support rich code navigation.

2. Language Manager (internal/ragcode/language_manager.go)

Purpose: Factory pattern for selecting the appropriate analyzer based on project type or language.

Key Functions:

func (m *AnalyzerManager) CodeAnalyzerForProjectType(projectType string) codetypes.PathAnalyzer
func (m *AnalyzerManager) APIAnalyzerForProjectType(projectType string) codetypes.APIAnalyzer

Supported Languages:

• LanguageGo (Go) - fully implemented
• LanguagePHP (PHP) - fully implemented with Laravel support
• LanguagePython (Python) - fully implemented with classes, decorators, type hints, mixins, metaclasses
• LanguageHTML (HTML) - basic support

4. Workspace Manager (internal/workspace/manager.go)

Purpose: Core component for multi-workspace and multi-language support. Manages automatic workspace detection, per-language collections, and multi-workspace indexing.

Key Capabilities:

• Automatic workspace detection using markers (.git, go.mod, package.json, etc.)
• Per-workspace, per-language collection creation: {prefix}-{workspaceID}-{language}
• Language detection from file markers
• Workspace cache for performance
• Multi-workspace simultaneous indexing with concurrency limits

Key Methods:

func (m *Manager) GetMemoryForWorkspaceLanguage(workspaceID, language string) (memory.LongTermMemory, error)
func (m *Manager) DetectWorkspace(params map[string]interface{}) (*Info, error)
func (m *Manager) GetAllWorkspaces() []Info

Example: For a monorepo with Go + Python code:

├── backend/                      → workspace "backend"
│   ├── .git/
│   ├── go.mod                   → language: "go"
│   └── Collections: ragcode-backend-go
├── frontend/                     → workspace "frontend"
│   ├── package.json             → language: "javascript"
│   └── Collections: ragcode-frontend-javascript
└── scripts/                      → workspace "scripts"
    ├── requirements.txt         → language: "python"
    └── Collections: ragcode-scripts-python

5. Workspace Detector (internal/workspace/detector.go)

Purpose: Detects workspace roots from file paths and manages workspace information caching.

Key Features:

• Find workspace root by looking for detection markers
• Cache workspace information for fast lookups
• Extract workspace metadata (root, ID, detected markers)

6. Language Detection (internal/workspace/language_detection.go)

Purpose: Identifies programming language from workspace detection markers.

Supported Languages (11+):

• Go: go.mod
• JavaScript/TypeScript/Node.js: package.json
• Python: pyproject.toml, setup.py, requirements.txt
• Rust: Cargo.toml
• PHP: composer.json
• Java: pom.xml, build.gradle
• Ruby: Gemfile
• Swift: Package.swift
• C#: *.csproj
• Others: .git alone indicates workspace root

5. Indexer (internal/ragcode/indexer.go)

Purpose: Indexes code chunks into vector database using embeddings.

Dependencies:

• Accepts codetypes.PathAnalyzer or codetypes.APIAnalyzer
• Uses llm.Provider for embeddings
• Stores in memory.LongTermMemory (Qdrant)

Workflow:

paths → analyzer.AnalyzePaths() → []CodeChunk → embeddings → Qdrant

6. Go Analyzer (internal/ragcode/analyzers/golang)

Purpose: Implements PathAnalyzer and APIAnalyzer for Go language using go/ast, go/doc, and go/parser.

Components:

• analyzer.go - Implements AnalyzePaths() for code chunk extraction
• api_analyzer.go - Implements AnalyzeAPIPaths() for API documentation
• types.go - Go-specific internal types (PackageInfo, FunctionInfo, TypeInfo, etc.)

Key Features:

• Extracts functions, methods, types, interfaces
• Populates Language: "go" for all chunks
• Supports docstring extraction
• Line-accurate positioning (StartLine, EndLine, SelectionRange)

Test Coverage: 82.1% (13 unit tests)

7. Storage: Qdrant Integration (internal/storage)

Purpose: Vector database integration for storing and retrieving embeddings.

Components:

• qdrant.go - Qdrant client wrapper with collection management
• qdrant_memory.go - LongTermMemory implementation using Qdrant

Features:

• Automatic collection creation
• Per-workspace, per-language collections
• Vector similarity search
• Filtering and text search integration

8. Tools: 8 MCP Tools (internal/tools)

Purpose: Implements semantic code navigation and search tools for IDE integration.

Tools:

1. search_local_index.go - Semantic search across indexed codebase
2. hybrid_search.go - Combined semantic + lexical search
3. get_function_details.go - Retrieve function signatures and documentation
4. find_type_definition.go - Locate type and interface definitions
5. get_code_context.go - Direct file access without indexing
6. list_package_exports.go - List exported symbols
7. find_implementations.go - Find interface implementations
8. search_docs.go - Search markdown documentation

All tools support:

• Workspace-specific queries
• Language-specific filtering
• Multi-language workspaces

Adding a New Language Analyzer

To add support for a new language (e.g., PHP, Python):

Step 1: Create Analyzer Package

mkdir -p internal/ragcode/analyzers/<language>

Step 2: Implement PathAnalyzer

Create analyzer.go:

package <language>

import "github.com/doITmagic/rag-code-mcp/internal/codetypes"

type CodeAnalyzer struct {
    // language-specific fields
}

func NewCodeAnalyzer() *CodeAnalyzer {
    return &CodeAnalyzer{}
}

func (ca *CodeAnalyzer) AnalyzePaths(paths []string) ([]codetypes.CodeChunk, error) {
    // Parse files and extract symbols
    // Set Language field to appropriate value (e.g., "php", "python")
    // Return chunks
}

Step 3: Implement APIAnalyzer

Create api_analyzer.go:

package <language>

import "github.com/doITmagic/rag-code-mcp/internal/codetypes"

type APIAnalyzerImpl struct {
    analyzer *CodeAnalyzer
}

func NewAPIAnalyzer(analyzer *CodeAnalyzer) *APIAnalyzerImpl {
    return &APIAnalyzerImpl{analyzer: analyzer}
}

func (a *APIAnalyzerImpl) AnalyzeAPIPaths(paths []string) ([]codetypes.APIChunk, error) {
    // Extract API documentation
    // Set Language field
    // Return API chunks
}

Step 4: Register in Language Manager

Update internal/ragcode/language_manager.go:

import "github.com/doITmagic/rag-code-mcp/internal/ragcode/analyzers/<language>"

const (
    Language<Name> Language = "<language>"
)

func (m *AnalyzerManager) CodeAnalyzerForProjectType(projectType string) codetypes.PathAnalyzer {
    lang := normalizeProjectType(projectType)
    switch lang {
    case Language<Name>:
        return <language>.NewCodeAnalyzer()
    // ...
    }
}

Step 5: Add Tests

Create analyzer_test.go and api_analyzer_test.go following the pattern in golang/ tests.

Step 6: Update Documentation

Update this file and main README.md to list the new language as supported.

Key Design Decisions

1. Separate codetypes Package

Rationale: Prevents import cycles. Analyzers import codetypes, not ragcode.

Benefits:

• Clean dependency graph: golang → codetypes, ragcode → codetypes, ragcode → golang
• Shared types accessible from all packages
• Easy to add new languages without circular dependencies

2. Language Field in All Chunks

Rationale: Support multi-language workspaces and language-specific queries.

Implementation: Each analyzer must set Language field (e.g., “go”, “php”, “python”) in all returned chunks.

3. LSP-Inspired Metadata

Rationale: Enable rich IDE-like features (navigation, hover, completion).

Fields Added:

• URI - Full document URI for protocol compliance
• SelectionRange - Precise symbol name location for “Go to Definition”
• Detail - Short description for hover tooltips
• AccessModifier - public/private/protected for filtering
• Tags - deprecated/experimental/internal for UI badges
• Children - Nested symbols for hierarchy display

4. Factory Pattern (Language Manager)

Rationale: Single point of entry for analyzer selection, easy to extend.

Benefits:

• Centralized language detection logic
• Consistent interface for all languages
• Easy to add language variants (e.g., “php-laravel”)

Testing Strategy

Unit Tests

• Test each analyzer independently with temporary test files
• Verify Language field is set correctly
• Check metadata accuracy (line numbers, signatures)
• Test edge cases (empty dirs, non-existent paths, interfaces)

Integration Tests

• Test full indexing pipeline (analyzer → embeddings → Qdrant)
• Verify search results match expectations
• Test workspace detection and multi-workspace scenarios

Coverage Goals

• Analyzers: >80% coverage
• Core packages: >70% coverage
• Tools: >60% coverage

Performance Considerations

Indexing

• Batch embedding calls to reduce latency
• Use goroutines for parallel file parsing
• Cache parsed ASTs when possible

Search

• Hybrid search combines vector + lexical for better results
• Limit results to top-k to reduce memory usage
• Use Qdrant’s filtering for language-specific queries

Multi-Language Configuration

config.yaml

workspace:
  enabled: true                    # Enable multi-workspace mode
  auto_index: true                 # Auto-index detected workspaces
  collection_prefix: ragcode       # Collection naming prefix
  
  # Language detection markers - file presence indicates language
  detection_markers:
    - .git                         # Generic workspace root
    - go.mod                       # Go projects
    - package.json                 # JavaScript/Node.js
    - pyproject.toml               # Python (modern)
    - setup.py                     # Python (legacy)
    - requirements.txt             # Python (pip)
    - composer.json                # PHP
    - Cargo.toml                   # Rust
    - pom.xml                      # Java (Maven)
    - build.gradle                 # Java (Gradle)
    - Gemfile                      # Ruby
    - Package.swift                # Swift

Environment Variables (Advanced)

For advanced users (not recommended for typical use):

• WORKSPACE_ENABLED - Enable/disable multi-workspace mode (default: true)
• WORKSPACE_AUTO_INDEX - Auto-index detected workspaces (default: true)
• WORKSPACE_COLLECTION_PREFIX - Collection naming prefix (default: “ragcode”)
• WORKSPACE_MAX_WORKSPACES - Maximum concurrent workspaces to index (default: 10)

Note: These variables are auto-managed by the system. Use defaults unless you have specific requirements.

Future Enhancements

Planned

• PHP analyzer implementation (PHP + Laravel analyzer, ~84% coverage, PAS 1–10 complete, production ready)
• Python analyzer implementation (placeholder ready)
• TypeScript/JavaScript analyzer
• Cross-language symbol references
• Multi-workspace search across all languages
• Language-specific embedding models

Under Consideration

• Incremental indexing (watch mode)
• Symbol relationship graph (calls, implements, extends)
• Code metrics and quality analysis
• Custom analyzer plugins via Go plugins

Current Implementation Status

Multi-Language Support: ✅ Fully implemented architecture

• Go: ✅ Fully implemented with 82% test coverage (13 unit tests)
• PHP: ✅ Fully implemented with 83.6% test coverage (19 unit tests)
  • Laravel Framework: ✅ Advanced framework support (14 integration tests)
• Python: 🔄 Placeholder - ready for implementation
• Other languages: Waiting for community contributions

Multi-Workspace Support: ✅ Fully implemented

• Automatic detection from 11+ language markers
• Per-workspace, per-language collections
• Concurrent multi-workspace indexing
• Comprehensive test suite (15+ integration tests)

MCP Tools: ✅ 8 tools fully implemented

• All tools support multi-workspace and multi-language queries
• Workspace-aware collection selection

PHP & Laravel Support

Overview

The PHP analyzer provides comprehensive support for PHP 8.0+ codebases with advanced Laravel framework integration.

PHP Base Analyzer (php/)

Features:

• ✅ Namespace and package detection
• ✅ Class extraction (properties, methods, constants)
• ✅ Interface extraction
• ✅ Trait extraction with usage detection
• ✅ Function extraction (global and methods)
• ✅ PHPDoc parsing for descriptions and types
• ✅ Visibility modifiers (public, protected, private)
• ✅ Type hints and return types
• ✅ AST-based analysis using VKCOM/php-parser

Test Coverage: 83.6% (19 unit tests)

Laravel Framework Support (php/laravel/)

Architecture:

php/laravel/
├── types.go              # Laravel-specific types
├── analyzer.go           # Main coordinator
├── eloquent.go           # Eloquent model analyzer
├── controller.go         # Controller analyzer
├── ast_helper.go         # AST extraction utilities
├── *_test.go             # Comprehensive test suite
└── README.md             # Documentation

Features:

1. Eloquent Model Analysis:

• ✅ Model detection (extends Illuminate\Database\Eloquent\Model)
• ✅ Property extraction: $table, $primaryKey, $fillable, $guarded, $casts, $hidden, $visible, $appends
• ✅ Trait detection: SoftDeletes, HasFactory, custom traits
• ✅ Relationship extraction: hasMany, hasOne, belongsTo, belongsToMany, morphMany, etc.
• ✅ Query scopes: scopeActive, scopePublished, etc.
• ✅ Accessors/Mutators: getFullNameAttribute, setPasswordAttribute
• ✅ AST-based property parsing (handles Post::class syntax)

2. Controller Analysis:

• ✅ Resource controller detection (7 CRUD methods: index, create, store, show, edit, update, destroy)
• ✅ API controller detection (namespace App\Http\Controllers\Api)
• ✅ Action extraction with HTTP method inference
• ✅ Parameter extraction (with $ prefix normalization)
• ✅ Custom action detection (non-CRUD methods)

3. AST Helpers:

• ✅ Property extraction: arrays, maps, strings from class properties
• ✅ Method call extraction: detects relation methods in model methods
• ✅ PHP variable name handling: automatic $ prefix trimming
• ✅ Class::class constant fetch support

Laravel Detection: The system automatically detects Laravel projects by checking for:

• Namespaces starting with App\Models, App\Http\Controllers
• Classes extending Model, Controller
• Illuminate\ framework classes

Test Coverage:

• 14 Laravel-specific tests (100% passing)
• 4 AST helper tests
• 3 Eloquent analyzer tests
• 4 Controller analyzer tests
• 3 Integration tests

Example Output:

// EloquentModel
{
  ClassName: "User",
  Namespace: "App\\Models",
  Table: "users",
  Fillable: ["name", "email", "password"],
  SoftDeletes: true,
  Relations: [
    {Name: "posts", Type: "hasMany", RelatedModel: "Post"},
    {Name: "profile", Type: "hasOne", RelatedModel: "Profile"}
  ],
  Scopes: [{Name: "active", MethodName: "scopeActive"}],
  Attributes: [{Name: "full_name", MethodName: "getFullNameAttribute"}]
}

// Controller
{
  ClassName: "PostController",
  Namespace: "App\\Http\\Controllers",
  IsResource: true,
  IsApi: false,
  Actions: [
    {Name: "index", HttpMethods: ["GET"]},
    {Name: "store", HttpMethods: ["POST"], Parameters: ["request"]},
    {Name: "destroy", HttpMethods: ["DELETE"], Parameters: ["post"]}
  ]
}

Usage:

// Detect Laravel project
analyzer := php.NewCodeAnalyzer()
analyzer.AnalyzeFile("app/Models/User.php")
if analyzer.IsLaravelProject() {
    // Get packages and analyze with Laravel
    packages := analyzer.GetPackages()
    laravelAnalyzer := laravel.NewAnalyzer(packages[0])
    info := laravelAnalyzer.Analyze()
    
    // info.Models contains Eloquent models
    // info.Controllers contains controllers
}

Contributing

When contributing code:

1. Follow the existing package structure
2. Implement both PathAnalyzer and APIAnalyzer for new languages
3. Add comprehensive tests (>80% coverage)
4. Update this architecture document
5. Set Language field correctly in all chunks
6. Use codetypes for shared types, not package-local definitions

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